Protein interactions

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DNA replication, transcription, translation, control of the cell cycle, signal transduction, secretory and metabolic processes. There are three main types of protein—protein interaction, termed surface—string, helix—helix and surface—surface interactions Figure Surface—surface interactions are the most common type of protein—protein contact. They require precise matching of complementary surfaces in the two proteins and tend to be relatively strong. The requirement for a good fit makes surface—surface interactions very specific.

In surface—string interactions, an extended loop of one protein makes contact with the surface of another protein. An example of this kind of interaction is that which occurs between a peptide containing a phosphotyrosine residue and the SH2 domain of Src and other related proteins. Another surface—string interaction takes place between the kinase fold of Src and part of the protein that it phosphorylates.

Such an interaction occurs in a number of gene-regulatory proteins. Towards the end of this free course we will look at some of the techniques employed to study protein—protein interactions.

Protein protein interaction

Making the decision to study can be a big step, which is why you'll want a trusted University. Take a look at all Open University courses. If you are new to university level study, find out more about the types of qualifications we offer, including our entry level Access courses and Certificates.

Not ready for University study then browse over free courses on OpenLearn and sign up to our newsletter to hear about new free courses as they are released. Every year, thousands of students decide to study with The Open University. OpenLearn works with other organisations by providing free courses and resources that support our mission of opening up educational opportunities to more people in more places. Over the course of many years of biological and biomedical research, a significant amount of information on CPIs has been discovered and published in scientific literature.

Since the CPI information published in scientific literature is stored as unstructured text that does not easily lend itself to automatic analysis and computation, a notable amount of scientific effort has been invested in manually extracting CPI information from literature and depositing it in a structured format into various databases. However, manually extracting CPI information is very time and resource consuming, and therefore relying on manual extraction methods is neither efficient nor sensible, given the speed of scientific publications.

Consequently, developing computational methods that can automatically extract such information is of great interest and urgency. A considerable number of computational methods have been developed to address a similar problem—extracting protein—protein interaction information PPI from literature 1— These approaches approached and addressed the problem using various techniques ranging from relatively simple co-occurrence, to rule-based pattern matching, to machine learning-based methods, which can be further enhanced by sophisticated natural language processing techniques.

A high-quality method to extract chemical—protein interactions will allow unstructured CPI information to be extracted efficiently from literature and deposited into databases to facilitate efficient querying of such information. The structured CPI information can also be used in integrative analysis of other biomedical data sets 19— Lastly, CPI extraction methods can be used in case studies of CPIs to get information on very specific types of chemical—protein interactions.

As a participant in this challenge, we developed a machine learning-based method for extracting CPI information from biomedical literature.

Modulators of Protein–Protein Interactions | Chemical Reviews

By combining various sets of features from semantic patterns and sentence structures with several machine learning classifiers, our method is capable of identifying the CPIs from a given text with satisfactory accuracy. The rest of the paper is organized as follows: we first describe the data used in this study, then present the proposed method and experimental results and end with a conclusion. The corpus used in this study comprises of PubMed articles.

All chemical compounds and proteins entities are tagged by the BioCreative VI track 5 organizers. There are a total of abstracts, in which entities are tagged and CPIs are annotated in the training and development set. The test set contains abstracts, tagged entities and CPIs. These five groups carry useful information for downstream applications. References 25 , 26 provide details on the definitions for each CPR and the guidelines used for CPI assignments in the corpus. A CPI pair is defined as a chemical entity and a protein entity in the same sentence, and a CPI triplet is defined as the combination of a CPI pair with an interaction word in the same sentence.

For each pair or triplet, feature sets were extracted from both the semantic pattern and the dependency graph of the sentence. The semantic pattern contains useful information on how the interaction between chemicals and proteins is described in the sentences. The dependency graph provides information on how words are interconnected in the sentence. Several classifiers were first trained on training set, then evaluated on the development set.


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Classifiers with top F 1 scores were selected for model building. The selected classifiers were retrained on the combined training and development sets before making predictions using the test set. A CPI pair is constructed with one chemical entity and one protein entity in a sentence. If a CPI pair is constructed but not included in the relation annotations, it will be labeled as CPR, which is defined as no interaction. If the sentence contains multiple interaction words, the same number of CPI triplets will be constructed, and they all share the same label. The interaction words in a sentence are informative for interaction extraction, since they may be used to describe the relationship between the chemical compound and the protein.

We have manually built an interaction words dictionary based on our previous study The dictionary contains interaction words. These interaction words were further manually mapped to the corresponding CPR type. Table 1 reports the number of pairs and triplets in each type of CPR used for evaluation.

Introduction

We extracted features based on our previous experiences and through reading some sentences in the training and development data sets. A feature was also created to catch such patterns.

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A detailed description of the features is provided in Table 2. We also employed features proposed in an earlier study These features capture certain grammar or language rules that people use to describe PPIs; we found that these rules are helpful in CPI extraction as well. This feature captures the negative meaning invoked by the sentence.

Introductory Chapter: Protein-Protein Interactions and Assays

This feature captures compound meanings in the sentence. The parser provides grammatical relationships among words in sentences, which are represented as dependency graphs where typed dependencies serve as edges between pairs of words nodes in the graphs. Dependency graphs can be effectively used to extract entity relations. One direct way to extract the relation between two words in a sentence is to find the shortest dependency path SDP in the graph constructed for the sentence For better parsing accuracy, entities of a CPI pair were replaced with chemical CHEM and protein PROT , respectively, in order to avoid the unnecessary complexity caused by multi-word terms when parsing a sentence.

Table 3 shows an example of a sentence after the entity names have been replaced. We added one-hot encoded-typed dependencies as features to indicate which dependencies are included in the SPD.


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We implemented a three-stage model building approach based on stacked generalization. Stacked generalization has proved to be effective in boosting the performance by reducing the generalization error 29 , At each stage, training data were first split into 10 disjoint folds. By taking one fold as holdout set and training a base model on the remaining folds, we obtained predictions for the holdout set and 10 different models. The holdout predictions will be used as an additional feature for the training data, and as a result, each training sample will have one more feature in the next stage of training.

Computational approaches for the analysis of RNA–protein interactions: A primer for biologists

When predicting test data, we repeat the same procedure. Since there are 10 different models from each fold in the fold training, each model will be used to predict test data and the predictions were averaged to give the additional feature for the test data, which is then used in the next stage of prediction. The complete flowchart of the three-stage model is given in Figure 2.

Tuning hyper-parameters and feature selection were performed at each stage. The hyper-parameters were tuned using randomized search. Under randomized search, the computational cost can be independent of the number, as well as the possible values, of the hyper-parameters The interactome is the totality of PPIs that happen in a cell, an organism or a specific biological context.

This experimental evidence is complemented by the availability of PPI prediction algorithms. A lot of this information is available through molecular interaction databases such as IntAct. Images reprinted by permission from Macmillan Publishers Ltd: Jeong et al. Nature Nature 4. It is important to emphasise once more the limitations of available PPI data.